I
nte
rna
t
io
na
l J
o
urna
l o
f
Ro
bo
t
ics a
nd
Aut
o
m
a
t
io
n (
I
J
R
A)
Vo
l.
8
,
No
.
1
,
Ma
r
ch
201
9
,
p
p
.
3
6
~
4
3
I
SS
N:
2089
-
4856
,
DOI
: 1
0
.
1
1
5
9
1
/
i
j
r
a
.
v
8
i
1
.
pp
3
6
-
4
3
36
J
o
ur
na
l ho
m
ep
a
g
e
:
h
ttp
:
//ia
e
s
co
r
e.
co
m/jo
u
r
n
a
ls
/in
d
ex
.
p
h
p
/
I
JR
A
A new
m
ethod
f
o
r hando
ff
targ
et
n
etw
o
rk
select
io
n
J
y
o
t
i
M
a
da
a
n
,
Su
na
nd
a
G
u
pta
,
P
ra
t
im
a
M
a
nih
a
s
M
a
n
a
v
Ra
c
h
n
a
I
n
t
e
r
n
a
t
i
o
n
a
l
I
n
s
t
i
tu
t
e
o
f
R
e
s
e
a
r
c
h
a
n
d
S
t
u
d
i
e
s
,
F
a
r
i
d
a
b
a
d
H
a
ry
a
n
a
,
N
e
w
D
e
l
h
i
Art
icle
I
nfo
AB
ST
RAC
T
A
r
ticle
his
to
r
y:
R
ec
eiv
ed
No
v
1
1
,
2
0
1
8
R
ev
i
s
ed
J
an
2
0
,
2
0
1
9
A
cc
ep
ted
Feb
5
,
2
0
1
9
In
w
irele
ss
c
o
m
m
u
n
ica
ti
o
n
s o
f
f
o
u
rth
g
e
n
e
ra
ti
o
n
t
h
e
e
x
p
e
c
tatio
n
to
a
ss
i
m
il
a
te
a
h
y
p
o
th
e
ti
c
a
ll
y
n
u
m
e
ro
u
s
h
e
tero
g
e
n
e
o
u
s
w
irele
ss
tec
h
n
o
lo
g
ies
a
re
h
a
p
p
e
n
e
d
u
n
d
e
r
c
o
n
sid
e
ra
ti
o
n
o
f
a
n
o
v
e
l
ste
p
to
w
a
rd
w
o
rld
w
id
e
s
m
o
o
th
a
c
c
e
ss
.
T
h
e
a
d
v
a
n
c
e
m
e
n
t
in
w
irele
ss
n
e
tw
o
r
k
s
in
c
re
a
s
e
s
th
e
c
h
a
ll
e
n
g
e
s
o
f
m
o
b
il
it
y
m
a
n
a
g
e
m
e
n
t
a
s
we
ll
th
e
c
h
a
ll
e
n
g
e
s
o
f
m
e
r
g
in
g
a
v
a
rio
u
s
n
u
m
b
e
r
o
f
w
irele
s
s
n
e
tw
o
rk
s.
Ou
t
o
f
th
o
se
th
e
m
a
i
n
c
h
a
ll
e
n
g
e
f
o
r
s
m
o
o
th
m
o
v
e
m
e
n
t
is
th
e
a
c
c
e
s
sib
il
it
y
o
f
c
o
n
siste
n
t
v
e
rti
c
a
l
(in
ters
y
ste
m
)
a
n
d
h
o
rizo
n
tal
(i
n
tr
a
-
s
y
ste
m
)
h
a
n
d
o
f
f
p
ro
c
e
ss
e
s.
S
o
to
im
p
ro
v
e
th
e
q
u
a
li
ty
o
f
se
r
v
ice
a
n
d
to
p
r
o
v
id
e
a
lw
a
y
s
b
e
st
c
o
n
n
e
c
ted
se
rv
ice
s
a
ll
th
e
t
im
e
,
th
e
h
a
n
d
o
f
f
d
e
c
isio
n
a
lg
o
rit
h
m
m
u
st
se
lec
t
a
n
o
p
ti
m
u
m
tar
g
e
t
n
e
tw
o
rk
f
ro
m
th
e
a
v
a
il
a
b
le
c
a
n
d
id
a
te
n
e
tw
o
rk
s.
T
h
e
p
u
rp
o
se
o
f
th
is
p
a
p
e
r
is
t
o
p
r
o
v
id
e
a
m
e
c
h
a
n
ism
f
o
r
se
l
e
c
ti
n
g
a
n
o
p
ti
m
u
m
targ
e
t
n
e
tw
o
rk
f
ro
m
th
e
a
v
a
il
a
b
le
n
e
tw
o
rk
s.
T
h
is
m
e
th
o
d
is
d
e
v
ise
d
f
o
r
m
a
x
i
m
izin
g
th
e
u
se
r
sa
ti
s
f
a
c
ti
o
n
lev
e
l,
b
y
se
lec
ti
n
g
th
e
“
b
e
st”
n
e
tw
o
rk
a
s
th
e
h
a
n
d
o
v
e
r
targ
e
t
n
e
tw
o
rk
a
m
o
n
g
m
u
lt
ip
le ca
n
d
i
d
a
te n
e
tw
o
rk
s
.
K
ey
w
o
r
d
s
:
A
l
w
a
y
s
b
est co
n
n
ec
ted
s
er
v
ic
es
(
A
B
C
)
Han
d
o
f
f
tar
g
et
n
et
w
o
r
k
s
elec
ti
o
n
a
n
d
ex
ec
u
tio
n
m
o
d
u
l
e
(
HT
SM)
Qu
alit
y
o
f
s
er
v
ice
(
Qo
S)
Un
i
v
er
s
al
m
o
b
ile
te
leco
m
m
u
n
icatio
n
s
er
v
ice
(
UM
T
S)
W
ir
e
less
lo
ca
l a
r
ea
n
et
w
o
r
k
(
W
L
A
N)
Co
p
y
rig
h
t
©
2
0
1
9
In
stit
u
te o
f
A
d
v
a
n
c
e
d
E
n
g
i
n
e
e
rin
g
a
n
d
S
c
ien
c
e
.
Al
l
rig
h
ts
re
se
rv
e
d
.
C
o
r
r
e
s
p
o
nd
ing
A
uth
o
r
:
J
y
o
ti M
ad
aa
n
,
Ma
n
av
R
ac
h
n
a
I
n
ter
n
atio
n
al
I
n
s
ti
tu
te
o
f
r
esear
ch
a
n
d
s
tu
d
ie
s
,
Far
id
ab
ad
Har
y
an
a,
121003
N
e
w
Del
h
i
.
E
m
ail:
J
y
o
ti
v
er
m
a.
f
et
@
m
r
iu
.
e
d
u
.
in
1.
I
NT
RO
D
UCT
I
O
N
No
w
ad
a
y
s
,
m
o
b
ile
co
m
m
u
n
ic
atio
n
h
as
h
eter
o
g
e
n
eo
u
s
w
ir
el
ess
n
et
w
o
r
k
s
o
f
f
er
in
g
v
ar
iab
le
co
v
er
ag
e
an
d
Qo
S.
Hete
r
o
g
en
eo
u
s
w
ir
eless
n
et
w
o
r
k
s
ar
e
o
n
e
o
f
t
h
e
m
o
s
t
i
m
p
o
r
ta
n
t
s
tr
u
ct
u
r
es
t
h
at
ar
e
n
ee
d
ed
f
o
r
d
ep
lo
y
m
en
t
o
f
w
ir
eles
s
tech
n
o
lo
g
ies
s
u
c
h
as
f
o
u
r
t
h
g
en
e
r
atio
n
(
4
G)
m
o
b
ile
s
y
s
te
m
s
.
T
h
e
h
eter
o
g
en
eo
u
s
w
ir
ele
s
s
n
e
t
w
o
r
k
is
a
m
ix
t
u
r
e
o
f
v
ar
io
u
s
ac
ce
s
s
tech
n
o
lo
g
y
to
allo
w
t
h
e
u
s
er
to
h
av
e
s
ea
m
les
s
m
o
b
ilit
y
an
d
b
est
q
u
alit
y
o
f
s
er
v
ice
at
all
t
i
m
e
s
u
c
h
a
s
h
i
g
h
co
v
er
a
g
e
o
f
c
ellu
lar
n
et
w
o
r
k
s
a
n
d
h
i
g
h
b
an
d
w
id
t
h
o
f
W
ir
eles
s
L
o
ca
l
A
r
ea
Net
w
o
r
k
(
W
L
A
N
)
[
1
-
2
]
.
B
u
t
to
allo
w
s
ea
m
les
s
m
o
b
ilit
y
a
n
d
al
w
a
y
s
b
est
co
n
n
ec
ted
s
er
v
ice
s
to
m
o
b
ile
u
s
er
,
th
er
e
is
a
n
e
ed
to
d
ev
elo
p
an
alg
o
r
ith
m
to
s
elec
t
a
n
o
p
ti
m
u
m
t
ar
g
et
n
et
w
o
r
k
f
r
o
m
th
e
av
ai
lab
le
n
et
w
o
r
k
.
So
m
e
s
o
l
u
tio
n
s
to
h
a
n
d
o
v
er
tar
g
et
s
elec
tio
n
alg
o
r
it
h
m
s
w
e
r
e
d
is
cu
s
s
ed
b
y
Z
h
u
a
n
d
Mc
Nair
[
3
-
5
]
.
T
h
e
au
th
o
r
s
h
a
v
e
p
r
o
p
o
s
ed
a
C
o
s
t
f
u
n
ctio
n
b
ased
alg
o
r
ith
m
to
s
elec
t
a
n
o
p
ti
m
u
m
tar
g
e
t
n
et
w
o
r
k
.
T
h
e
to
tal
co
s
t
is
th
e
s
u
m
o
f
th
e
co
s
t
o
f
ea
ch
Qo
S
p
ar
a
m
e
ter
,
in
cl
u
d
in
g
t
h
e
b
a
n
d
w
id
th
,
b
atter
y
p
o
w
er
a
n
d
d
ela
y
.
I
t
ca
lcu
late
s
a
co
s
t
f
o
r
all
t
h
e
ca
n
d
id
ate
n
et
w
o
r
k
s
b
ased
o
n
s
tatic
a
n
d
d
y
n
a
m
ic
p
ar
a
m
eter
s
.
T
h
e
n
et
w
o
r
k
w
it
h
th
e
m
i
n
i
m
u
m
co
s
t
is
s
e
lecte
d
as
th
e
h
an
d
o
v
er
tar
g
et
n
e
t
w
o
r
k
.
T
h
is
m
et
h
o
d
h
as
i
n
cr
ea
s
e
d
th
e
p
er
ce
n
tag
e
o
f
u
s
er
s
ati
s
f
ied
r
eq
u
ests
a
n
d
r
ed
u
ce
d
th
e
ca
ll
b
lo
ck
i
n
g
p
r
o
b
ab
ilit
y
.
Ho
w
ev
er
,
t
h
e
au
t
h
o
r
s
d
id
n
o
t
d
is
cu
s
s
h
o
w
th
e
w
ei
g
h
t
s
f
o
r
t
h
e
Qo
S f
ac
to
r
s
w
er
e
as
s
i
g
n
ed
a
n
d
n
o
r
m
alize
d
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t
J
R
o
b
&
A
u
to
m
I
SS
N:
2089
-
4856
A
n
ew me
th
o
d
fo
r
h
a
n
d
o
ff ta
r
g
et
n
etw
o
r
k
s
elec
tio
n
(
Jy
o
ti M
a
d
a
a
n
)
37
2.
O
VE
RVI
E
W
O
F
H
ANDO
F
F
T
AR
G
E
T
NE
T
WO
RK
S
E
L
E
C
T
I
O
N
A
ND
E
X
E
CU
T
I
O
N
M
O
DULE
(
H
T
SM
)
HT
SM
alg
o
r
ith
m
s
elec
t
s
an
o
p
tim
u
m
tar
g
et
n
e
t
w
o
r
k
f
r
o
m
t
h
e
av
ai
lab
le
ca
n
d
id
ate
n
et
w
o
r
k
s
t
o
p
r
o
v
id
e
A
l
w
a
y
s
B
est
C
o
n
n
ec
t
ed
Ser
v
ices
(
A
B
C
)
to
u
s
er
s
.
I
t
co
n
s
is
t
s
o
f
t
w
o
u
n
it
s
-
W
ei
g
h
t
E
s
ti
m
a
tio
n
u
n
i
t
an
d
Han
d
o
f
f
Facto
r
E
s
ti
m
at
io
n
u
n
it.
Fig
u
r
e
1
s
h
o
w
s
th
e
b
lo
ck
d
iag
r
a
m
o
f
HT
SM.
T
h
e
H
T
SM
m
o
d
u
le
tak
es
t
h
e
in
p
u
t
f
r
o
m
W
eig
h
t
E
s
t
i
m
a
tio
n
u
n
it
a
n
d
Han
d
o
f
f
Facto
r
E
s
ti
m
atio
n
u
n
it
a
n
d
g
en
er
ate
s
a
n
o
u
tp
u
t
d
ep
en
d
i
n
g
u
p
o
n
th
e
s
co
r
e
attain
ed
b
y
a
ca
n
d
id
ate
n
et
w
o
r
k
s
.
Fig
u
r
e
1
.
Han
d
o
f
f
T
ar
g
et
Net
w
o
r
k
Se
lectio
n
a
n
d
E
x
ec
u
tio
n
Mo
d
u
le
(
HT
SM)
2
.
1
.
Weig
hts Es
t
i
m
a
t
io
n
U
ni
t
T
h
e
W
eig
h
ts
E
s
ti
m
atio
n
u
n
it
tak
es
a
v
ailab
le
b
an
d
w
id
t
h
,
s
e
r
v
ice
co
s
t
an
d
u
s
er
p
r
ef
er
en
c
e
as
in
p
u
t
d
ec
is
io
n
p
ar
a
m
eter
s
,
a
n
d
g
e
n
er
ates
w
ei
g
h
t
f
ac
to
r
s
f
o
r
t
h
ese
h
an
d
o
v
er
d
ec
is
io
n
p
ar
am
eter
s
a
s
o
u
tp
u
ts
.
W
eig
h
t
f
ac
to
r
s
o
f
h
an
d
o
v
er
d
ec
is
io
n
p
ar
am
e
ter
s
s
h
o
w
t
h
e
im
p
o
r
tan
ce
le
v
els
f
o
r
th
e
n
et
w
o
r
k
p
ar
a
m
eter
s
.
W
eig
h
t
E
s
t
i
m
a
tio
n
u
n
it
ca
lc
u
l
ates
w
e
ig
h
t
f
ac
to
r
s
o
f
d
ec
is
io
n
p
ar
am
eter
s
(
av
ai
lab
le
b
an
d
w
i
d
th
,
m
o
n
etar
y
co
s
t,
an
d
u
s
er
p
r
ef
er
e
n
ce
)
u
s
i
n
g
a
m
et
h
o
d
d
escr
ib
ed
b
elo
w
.
T
h
e
m
et
h
o
d
as
s
u
m
e
s
t
h
at
th
e
w
e
ig
h
t
f
ac
to
r
s
o
f
t
h
e
n
et
w
o
r
k
p
ar
a
m
eter
s
,
av
a
ilab
l
e
b
an
d
w
id
th
,
m
o
n
etar
y
co
s
t
,
an
d
u
s
er
p
r
ef
er
en
ce
,
ar
e
W
BW
,
W
CO
,
W
UP
,
r
esp
ec
tiv
el
y
.
T
h
e
v
a
lu
e
s
o
f
t
h
ese
w
ei
g
h
ts
ar
e
f
r
ac
tio
n
a
n
d
t
h
e
s
u
m
m
atio
n
o
f
all
w
eig
h
t
s
ca
n
b
e
u
p
to
o
n
e.
W
h
er
e
W
BW
+W
CO
+W
UP
=
1.
Si
n
ce
ea
ch
n
et
w
o
r
k
p
ar
a
m
eter
h
as
a
d
if
f
er
en
t
u
n
i
t,
it
is
n
ec
e
s
s
ar
y
to
n
o
r
m
al
ize
th
e
m
.
T
h
e
n
o
r
m
alize
d
v
alu
e
o
f
in
p
u
t
p
ar
a
m
eter
s
(
B
an
d
w
id
th
,
C
o
s
t,
an
d
User
p
r
ef
er
e
n
ce
)
ca
n
b
e
ca
lcu
lated
as
(1
-
3)
[
6
-
8
]:
(
1)
(
2
)
(
3
)
W
h
er
e
B
W
m
ax
an
d
B
W
m
in
a
r
e
th
e
m
ax
i
m
u
m
a
n
d
m
in
i
m
u
m
b
a
n
d
w
id
th
o
f
f
er
ed
b
y
n
et
w
o
r
k
l.
C
m
ax
a
n
d
C
m
in
ar
e
th
e
m
a
x
i
m
u
m
a
n
d
m
i
n
i
m
u
m
c
h
ar
g
e
s
o
f
a
s
er
v
ice.
T
h
e
u
s
er
p
r
ef
er
en
ce
(
UP
)
r
an
g
e
is
f
r
o
m
0
to
1
0
.
User
p
r
ef
er
en
ce
is
h
ig
h
,
w
h
en
a
u
s
er
p
r
ef
er
s
to
s
e
lect
W
L
A
N,
a
n
d
it
is
lo
w
i
f
a
u
s
e
r
p
r
ef
er
s
to
s
ta
y
in
UM
T
S.
Fo
r
T
ca
n
d
id
ate
n
et
w
o
r
k
s
Me
an
a
n
d
Sta
n
d
ar
d
Dev
i
atio
n
o
f
b
an
d
w
id
t
h
,
co
s
t,
an
d
u
s
er
p
r
ef
er
en
ce
ca
n
b
e
ca
lcu
lated
as
(4
-
9)
[9
,
11
]:
(
4
)
l
m
i
n
l
m
a
x
m
i
n
B
W
B
W
N
(
B
W
)
B
W
B
W
l
m
i
n
l
m
a
x
m
i
n
C
O
C
O
N
(
C
O
)
C
O
C
O
l
m
i
n
l
m
a
x
m
i
n
U
P
U
P
N
(
U
P
)
U
P
U
P
T
B
W
t
t1
1
m
N
(
B
W
)
T
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
9
-
4856
I
n
t
J
R
ob
&
A
u
to
m
,
Vo
l.
8
,
No
.
1
,
Ma
r
ch
2
0
1
9
:
3
6
–
4
3
38
(
5
)
(
6)
(
7
)
(
8
)
(
9
)
I
n
f
ac
t,
t
h
e
s
m
a
ller
m
ea
n
is
,
t
h
e
m
o
r
e
i
m
p
o
r
ta
n
t
t
h
e
f
ac
to
r
i
s
.
T
h
e
lar
g
er
t
h
e
s
ta
n
d
ar
d
d
ev
iatio
n
i
s
,
t
h
e
lar
g
er
th
e
w
ei
g
h
t
s
h
o
u
ld
b
e
ass
i
g
n
ed
.
I
t le
ad
s
to
ad
j
u
s
t th
e
w
ei
g
h
ts
d
y
n
a
m
icall
y
b
y
(
1
0
-
12)
(
10)
(
1
1
)
(
1
2
)
L
etti
n
g
,
th
e
d
y
n
a
m
ic
w
ei
g
h
ts
ar
e
d
ef
in
ed
as
(
1
3
-
15)
D
y
n
a
m
ic
w
e
ig
h
t
f
o
r
B
an
d
w
id
t
h
(
13)
D
y
n
a
m
ic
w
e
ig
h
t
f
o
r
C
o
s
t
(
14)
D
y
n
a
m
ic
w
e
ig
h
t
f
o
r
User
p
r
ef
er
en
ce
(
15)
w
h
er
e
W
BW
,
W
CO
,
an
d
W
UP
ar
e
th
e
w
ei
g
h
t
f
ac
to
r
s
to
r
ep
r
esen
t
t
h
e
i
m
p
o
r
tan
ce
o
f
ea
c
h
m
etr
ic
to
th
e
u
s
er
.
T
h
r
o
u
g
h
eq
u
atio
n
1
3
to
1
5
,
th
e
m
o
b
ile
n
o
d
e
is
ab
le
to
ass
ig
n
w
ei
g
h
ts
to
th
e
n
et
w
o
r
k
p
ar
a
m
eter
s
d
y
n
a
m
icall
y
.
2
.
2
.
H
a
nd
o
f
f
F
a
ct
o
r
E
s
t
i
m
a
t
io
n
T
h
e
Han
d
o
f
f
Facto
r
(
HF)
,
wh
ich
p
r
o
v
id
es
a
m
ea
s
u
r
e
m
e
n
t
o
f
t
h
e
co
s
t
o
f
a
ce
r
tai
n
n
et
wo
r
k
ca
n
b
e
d
ef
in
ed
as
an
i
m
p
r
o
v
e
m
e
n
t
g
ain
ed
b
y
t
h
e
u
s
er
af
ter
s
w
itc
h
in
g
to
a
n
e
w
n
et
w
o
r
k
r
eg
ar
d
in
g
to
th
e
r
u
n
n
in
g
s
er
v
ices.
T
h
e
h
a
n
d
o
f
f
f
ac
to
r
c
alcu
latio
n
al
g
o
r
ith
m
e
v
al
u
ates
th
e
co
s
t
o
f
ca
n
d
id
ate
n
et
w
o
r
k
s
b
y
u
s
i
n
g
a
co
s
t
f
u
n
ctio
n
f
o
r
m
a
k
i
n
g
a
h
a
n
d
o
f
f
.
I
t
tak
e
s
v
ar
io
u
s
n
e
t
w
o
r
k
p
ar
a
m
eter
s
a
n
d
t
h
eir
w
e
ig
h
ts
as
in
p
u
ts
a
n
d
g
en
er
ate
s
h
an
d
o
f
f
f
ac
to
r
s
f
o
r
all
ca
n
d
id
ate
n
et
w
o
r
k
s
.
T
h
e
n
et
w
o
r
k
with
t
h
e
h
i
g
h
e
s
t
h
an
d
o
f
f
f
ac
to
r
is
s
e
lecte
d
as
a
n
o
p
tim
u
m
tar
g
et
n
et
w
o
r
k
.
T
h
e
Han
d
o
f
f
Facto
r
(
HF)
o
f
a
ce
r
tain
n
et
w
o
r
k
l,
is
ca
l
cu
lated
u
s
i
n
g
th
e
f
o
llo
w
in
g
f
u
n
ctio
n
(
1
6
)
[
1
0
,
7
]
:
(1
6
)
T
C
O
t
t1
1
m
N
(
C
O
)
T
T
U
P
t
t1
1
m
N
(
U
P
)
T
T
B
W
t
B
W
t1
1
(
)
(
T
(
B
W
)
m
)
T1
T
c
o
t
c
o
t1
1
(
)
(
T
(
c
o
)
m
)
T1
T
U
P
t
U
P
t1
1
(
)
(
T
(
U
P
)
m
)
T1
B
W
B
W
B
W
e
x
p
(
m
)
C
O
C
O
C
O
e
x
p
(
m
)
U
P
U
P
U
P
e
x
p
(
m
)
B
W
C
O
U
P
BW
BW
(
W
)
CO
CO
(
W
)
UP
UP
(
W
)
B
W
l
c
o
l
l
1T
1T
U
P
l
1T
W
B
W
W
1
/
C
O
HF
m
a
x
B
W
,
.
.
.
.
.
.
.
B
W
11
m
a
x
,
.
.
.
.
.
.
.
C
O
C
O
W
U
P
m
a
x
U
P
,
.
.
.
.
.
.
.
U
P
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t
J
R
o
b
&
A
u
to
m
I
SS
N:
2089
-
4856
A
n
ew me
th
o
d
fo
r
h
a
n
d
o
ff ta
r
g
et
n
etw
o
r
k
s
elec
tio
n
(
Jy
o
ti M
a
d
a
a
n
)
39
W
h
er
e
B
W
l
,
C
O
l
,
UP
l
s
tan
d
s
f
o
r
av
ailab
le
b
an
d
w
id
th
(
i
n
Mb
p
s
)
,
m
o
n
etar
y
co
s
t
p
er
m
in
u
te
(
i
n
ce
n
ts
)
,
a
n
d
u
s
er
p
r
e
f
er
en
ce
(
o
n
a
s
ca
le
o
f
1
to
1
0
,
f
r
o
m
v
er
y
lo
w
to
v
er
y
h
i
g
h
)
,
an
d
W
BW
,
W
CO
an
d
W
PO
ar
e
th
eir
w
e
ig
h
t
s
o
b
tain
ed
f
r
o
m
th
e
w
ei
g
h
t
es
ti
m
a
tio
n
al
g
o
r
ith
m
.
L
astl
y
,
th
e
n
et
w
o
r
k
w
i
th
a
H
an
d
o
f
f
Facto
r
(
HF)
o
f
m
a
x
(
HF
l
; :
: ;H
F
T
)
is
s
elec
t
ed
as th
e
h
a
n
d
o
v
er
tar
g
et.
W
h
er
e
T
is
th
e
to
tal
n
u
m
b
er
o
f
ca
n
d
id
ate
n
et
w
o
r
k
s
.
3.
M
E
T
H
O
DO
L
O
G
Y
T
h
e
Han
d
o
f
f
T
ar
g
et
Net
w
o
r
k
Selectio
n
&
E
x
ec
u
tio
n
Mo
d
u
le
(
HT
SM)
is
u
s
ed
to
s
elec
t
th
e
b
est
tar
g
et
n
e
t
w
o
r
k
f
r
o
m
t
h
e
av
ai
l
ab
le
n
et
w
o
r
k
s
.
I
n
T
ar
g
et
n
et
w
o
r
k
s
elec
tio
n
p
r
o
ce
s
s
all
t
h
e
ca
n
d
id
ate
n
et
w
o
r
k
s
ar
e
an
al
y
ze
d
i
n
ter
m
s
o
f
b
an
d
w
id
t
h
,
co
s
t,
a
n
d
u
s
er
p
r
ef
er
en
ce
.
Fig
u
r
e
2
s
h
o
w
s
t
h
e
o
v
er
all
w
o
r
k
f
lo
w
o
f
HT
SM
alg
o
r
ith
m
.
I
n
th
i
s
,
f
ir
s
t
o
f
all
w
ei
g
h
ts
ar
e
ca
lcu
lated
to
in
p
u
t
d
ec
is
io
n
p
ar
am
eter
s
.
Af
ter
th
at,
th
e
n
et
w
o
r
k
is
an
al
y
ze
d
b
ased
o
n
Ha
n
d
o
f
f
Fa
cto
r
.
T
h
e
Han
d
o
f
f
Facto
r
is
a
f
u
n
ct
io
n
o
f
a
v
ailab
le
b
an
d
w
id
t
h
(
B
W
)
,
u
s
ag
e
co
s
t
o
f
th
e
n
et
w
o
r
k
(
C
O)
,
a
n
d
u
s
e
r
p
r
ef
er
en
c
e
(
UP
)
.
T
h
e
Han
d
o
f
f
Facto
r
(
HF)
i
s
a
s
co
r
e
f
u
n
c
tio
n
,
w
h
ich
tak
e
s
a
f
i
n
al
d
ec
is
io
n
o
f
s
e
lecti
n
g
a
b
est
ca
n
d
id
ate
n
et
w
o
r
k
f
r
o
m
a
s
et
o
f
ca
n
d
id
ate
n
et
w
o
r
k
s
.
A
ca
n
d
id
ate
n
et
w
o
r
k
,
w
h
ic
h
h
as
th
e
h
ig
h
e
s
t
v
al
u
e
o
f
“
Ha
n
d
o
f
f
Fa
cto
r
(
HF)
”
i
s
s
el
ec
ted
as
t
h
e
b
es
t
i
n
ter
f
ac
e
f
o
r
h
an
d
o
f
f
an
d
a
ll
t
h
e
cu
r
r
en
t i
n
f
o
r
m
atio
n
o
f
th
e
c
u
r
r
en
t n
et
w
o
r
k
a
r
e
tr
an
s
p
o
r
ted
to
n
e
w
s
elec
ted
tar
g
e
t n
et
w
o
r
k
.
Fig
u
r
e
2
.
Flo
w
c
h
ar
t o
f
T
ar
g
et
Net
w
o
r
k
Selectio
n
&
E
x
ec
u
t
i
o
n
Mo
d
u
le
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
9
-
4856
I
n
t
J
R
ob
&
A
u
to
m
,
Vo
l.
8
,
No
.
1
,
Ma
r
ch
2
0
1
9
:
3
6
–
4
3
40
4.
SI
M
UL
AT
I
O
N
SE
T
UP
MA
T
L
A
B
Ver
s
io
n
7
.
1
2
.
0
.
5
3
5
(
R
2
0
1
1
a)
is
u
s
ed
to
g
e
n
er
ate
1
0
0
0
s
ets
o
f
r
an
d
o
m
tr
aj
e
cto
r
y
o
f
a
m
o
b
ile
n
o
d
e
w
it
h
r
a
n
d
o
m
v
e
lo
cit
y
f
r
o
m
1
to
5
0
m
/
s
.
I
n
t
h
e
s
i
m
u
latio
n
,
an
o
v
er
laid
ar
ch
itect
u
r
e
o
f
s
i
n
g
le
UM
T
S,
f
o
u
r
teen
W
L
A
N
an
d
th
r
ee
W
i
-
M
AX
ar
e
co
n
s
id
er
ed
to
co
v
er
an
a
re
a
o
f
3
0
0
0
*
3
0
0
0
m
a
s
s
h
o
w
n
i
n
F
ig
u
r
e
3
.
T
h
e
tr
an
s
m
i
s
s
io
n
r
a
n
g
e
o
f
UM
T
S
co
v
er
s
an
ar
ea
o
f
3
0
0
0
m
,
W
i
-
M
A
X
co
v
er
s
an
ar
ea
o
f
1
0
0
0
m
an
d
W
L
A
N
co
v
er
s
an
ar
ea
o
f
2
5
0
m
.
T
h
e
b
an
d
w
id
t
h
o
f
UM
T
S,
W
L
AN
an
d
W
i
-
M
A
X
ar
e
3
8
4
k
b
/s
,
1
1
Mb
/s
,
an
d
1
5
Mb
/s
,
r
esp
ec
tiv
el
y
.
T
h
e
n
u
m
b
er
o
f
m
o
b
ile
n
o
d
es
r
an
g
e
s
f
r
o
m
1
to
1
0
,
an
d
ar
e
co
n
f
i
g
u
r
ed
to
u
s
e
in
ter
f
ac
e
s
UM
T
S,
W
i
-
Fi,
a
n
d
W
i
-
M
A
X.
T
h
e
r
ec
eiv
ed
s
i
g
n
al
s
tr
en
g
t
h
is
s
a
m
p
led
at
e
v
er
y
0
.
1
s
ec
.
T
h
e
v
ar
io
u
s
s
i
m
u
lat
io
n
p
ar
a
m
eter
s
a
n
d
n
et
w
o
r
k
p
ar
a
m
eter
s
co
n
s
id
er
ed
f
o
r
s
i
m
u
la
tio
n
ar
e
d
ef
i
n
ed
in
T
ab
le
1
an
d
T
ab
le
2
.
Fig
u
r
e
3.
Ov
er
laid
W
ir
eless
N
et
w
o
r
k
o
f
W
L
A
N,
W
i
-
M
A
X
a
n
d
UM
T
S
T
ab
le
1.
Sim
u
latio
n
p
ar
a
m
eter
s
S
.
N
o
.
S
i
mu
l
a
t
i
o
n
P
a
r
a
me
t
e
r
s
V
a
l
u
e
s
1
T
o
p
o
l
o
g
y
S
i
z
e
(
me
t
e
r
)
3
0
0
0
*
3
0
0
0
2
N
u
mb
e
r
o
f
mo
b
i
l
e
n
o
d
e
s
1
~
1
0
3
N
u
mb
e
r
o
f
W
L
A
N
s
14
4
N
u
mb
e
r
o
f
W
M
A
N
s
3
5
N
u
mb
e
r
o
f
U
M
T
S
1
6
P
a
t
h
l
o
ss c
o
n
s
t
a
n
t
,
Z
5
7
P
a
t
h
l
o
ss e
x
p
o
n
e
n
t
,
β
3
.
5
T
ab
le
2
.
Netw
o
r
k
p
ar
a
m
eter
s
S
.
N
o
N
e
t
w
o
r
k
p
a
r
a
me
t
e
r
s
W
L
A
N
Wi
-
M
A
X
U
M
T
S
1
B
a
n
d
w
i
d
t
h
(
mi
n
-
max
(
M
b
p
s)
)
1
-
A
p
r
2
-
Ju
n
0
.
1
-
0
.
3
2
C
o
st
(
mi
n
-
max
)
0
.
1
-
0
.
4
0
.
3
-
0
.
5
0
.
7
-
2
.
5
3
U
se
r
p
r
e
f
e
r
e
n
c
e
5
t
o
1
0
5
t
o
1
0
0
t
o
5
4
M
o
b
i
l
e
n
o
d
e
v
e
l
o
c
i
t
y
(
m/
s)
<
3
<
3
3
<
8
0
5
T
r
a
n
smissi
o
n
r
a
n
g
e
(
m)
2
5
0
1
0
0
0
3
0
0
0
5.
RE
SU
L
T
S AN
D
AN
AL
Y
SI
S
T
h
e
p
r
o
p
o
s
ed
m
o
d
el
tr
ig
g
er
s
th
e
h
a
n
d
o
f
f
at
a
n
ap
p
r
o
p
r
iate
ti
m
e
d
ep
en
d
i
n
g
u
p
o
n
t
h
e
s
et
o
f
p
o
licies
d
ef
in
ed
f
o
r
W
L
A
N
a
n
d
UM
T
S
an
d
s
elec
ts
an
o
p
ti
m
u
m
tar
g
et
n
et
w
o
r
k
at
ea
ch
p
o
in
t
w
h
ich
h
a
s
th
e
h
i
g
h
est
v
alu
e
o
f
Ha
n
d
o
f
f
Facto
r
d
u
r
i
n
g
its
m
o
v
e
m
e
n
t.
Her
e,
t
h
e
s
i
m
u
lated
r
es
u
lt
s
ar
e
s
h
o
w
n
f
o
r
th
r
ee
d
if
f
er
e
n
t
r
an
d
o
m
tr
aj
ec
to
r
ies o
f
m
o
b
ile
n
o
d
es.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t
J
R
o
b
&
A
u
to
m
I
SS
N:
2089
-
4856
A
n
ew me
th
o
d
fo
r
h
a
n
d
o
ff ta
r
g
et
n
etw
o
r
k
s
elec
tio
n
(
Jy
o
ti M
a
d
a
a
n
)
41
5
.
1
.
F
o
r
m
o
bil
e
no
de
m
o
v
e
m
e
nt
f
ro
m
po
i
nt
A
t
o
B
I
n
itiall
y
,
w
h
en
s
i
m
u
latio
n
s
tar
ts
m
o
b
ile
n
o
d
e
is
co
n
n
e
cted
to
UM
T
S
n
et
w
o
r
k
at
p
o
in
t
A.
W
h
en
m
o
b
ile
n
o
d
e
m
o
v
es
f
r
o
m
p
o
in
t
A
to
p
o
in
t
B
as
s
h
o
w
n
i
n
F
ig
u
r
e
4
,
it
r
ec
eiv
e
s
s
i
g
n
al
f
r
o
m
t
h
e
ei
g
h
t
ca
n
d
id
ate
n
et
w
o
r
k
s
W
i
-
M
AX
2
,
W
i
-
M
A
X
3
,
W
L
A
N
3
,
W
L
A
N
4
,
W
L
A
N
8
,
W
L
A
N
1
2
,
W
L
A
N
1
3
,
W
L
A
N
1
4
.
T
h
er
ef
o
r
e,
Han
d
o
f
f
alg
o
r
it
h
m
i
s
tr
ig
g
er
ed
at
th
i
s
p
o
in
t
d
u
e
to
d
is
tin
ct
s
i
g
n
al
s
tr
en
g
t
h
,
b
an
d
w
id
t
h
,
co
s
t,
an
d
u
s
er
p
r
ef
er
en
ce
.
H
an
d
o
f
f
f
ac
to
r
is
ca
lcu
lated
a
t
th
i
s
p
o
in
t
f
o
r
all
th
e
ei
g
h
t
elig
ib
le
ca
n
d
id
ate
n
et
w
o
r
k
s
.
At
th
i
s
p
o
in
t,
W
L
AN
4
is
p
r
ef
er
r
ed
n
et
w
o
r
k
b
ec
au
s
e
o
f
h
i
g
h
h
an
d
o
f
f
f
ac
to
r
.
T
ab
le
3
s
h
o
w
s
t
h
at,
th
e
p
r
ese
n
ted
m
o
d
el
s
elec
ts
t
h
e
tar
g
et
n
et
w
o
r
k
f
r
o
m
th
e
c
an
d
id
ate
n
e
t
w
o
r
k
d
u
r
in
g
m
o
b
ile
n
o
d
e
m
o
v
e
m
e
n
t
f
r
o
m
p
o
in
t
A
to
B
.
Fig
u
r
e
4
.
Mo
b
ile
No
d
e
M
o
v
em
en
t f
r
o
m
P
o
in
t
A
to
B
T
ab
le
3
.
P
r
ef
er
r
ed
Netw
o
r
k
(
Mo
b
ile
No
d
e
Mo
v
em
e
n
t
f
r
o
m
P
o
in
t A
to
B
)
M
o
b
i
l
e
N
o
d
e
L
o
c
a
t
i
o
n
WB
WC
WB
C
a
n
d
i
d
a
t
e
N
e
t
w
o
r
k
H
a
n
d
o
f
f
f
a
c
t
o
r
P
r
e
f
e
r
r
e
d
N
e
t
w
o
r
k
D
i
st
a
n
c
e
w
h
i
c
h
H
a
n
d
o
f
f
O
c
c
u
r
B
0
.
0
1
5
8
0
.
5
1
0
5
0
.
3
8
3
7
Wi
-
M
A
X
2
0
.
0
1
2
4
8
W
L
A
N
4
1
0
0
0
,
5
0
Wi
-
M
A
X
3
0
.
2
7
3
7
4
W
L
A
N
3
0
.
9
9
4
7
4
W
L
A
N
4
1
.
5
0
8
W
L
A
N
8
1
.
3
4
7
W
L
A
N
1
2
1
.
4
5
8
4
W
L
A
N
1
3
0
.
9
4
9
5
5
W
L
A
N
1
4
1
.
4
3
7
1
5
.
1
.
F
o
r
m
o
bil
e
no
de
m
o
v
e
m
e
nt
f
ro
m
B
t
o
C
As
s
h
o
w
n
i
n
F
i
g
u
r
e
5
,
w
h
e
n
m
o
b
ile
n
o
d
e
m
o
v
es
f
r
o
m
p
o
in
t
B
to
p
o
in
t
C
.
T
h
e
m
o
b
ile
n
o
d
e
r
ec
eiv
e
s
s
ig
n
al
f
r
o
m
W
i
-
M
AX
2
,
W
i
-
M
A
X
3
,
W
L
A
N
3
,
W
L
AN
4
,
W
L
A
N
8
,
an
d
W
L
A
N
1
2
,
A
t
t
h
i
s
p
o
in
t,
Wi
-
M
A
X
2
i
s
p
r
ef
er
r
ed
n
e
t
wo
r
k
b
ec
au
s
e
o
f
h
i
g
h
h
a
n
d
o
f
f
f
ac
to
r
.
T
a
b
le
4
s
h
o
w
s
t
h
e
ca
n
d
id
ate
n
et
w
o
r
k
a
n
d
elig
ib
le
n
e
t
w
o
r
k
at
p
o
in
t C.
Fig
u
r
e
5
.
Mo
b
ile
No
d
e
M
o
v
em
en
t f
r
o
m
P
o
in
t B
to
C
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
9
-
4856
I
n
t
J
R
ob
&
A
u
to
m
,
Vo
l.
8
,
No
.
1
,
Ma
r
ch
2
0
1
9
:
3
6
–
4
3
42
T
ab
le
4
.
P
r
ef
er
r
ed
Netw
o
r
k
(
Mo
b
ile
No
d
e
Mo
v
em
e
n
t
f
r
o
m
P
o
in
t B
to
C
)
M
o
b
i
l
e
N
o
d
e
L
o
c
a
t
i
o
n
WB
WC
WB
C
a
n
d
i
d
a
t
e
N
e
t
w
o
r
k
H
a
n
d
o
f
f
f
a
c
t
o
r
P
r
e
f
e
r
r
e
d
N
e
t
w
o
r
k
D
i
st
a
n
c
e
w
h
i
c
h
H
a
n
d
o
f
f
O
c
c
u
r
C
0
.
0
2
0
5
5
0
.
5
7
8
5
0
.
3
1
5
0
Wi
-
M
A
X
2
1
.
8
1
8
1
Wi
-
M
A
X
2
2
0
0
.
3
5
0
Wi
-
M
A
X
3
1
.
8
0
8
1
W
L
A
N
3
0
.
3
7
7
W
L
A
N
4
1
.
0
8
1
8
W
L
A
N
8
0
.
5
8
0
5
1
W
L
A
N
1
2
0
.
4
5
0
4
1
W
L
A
N
1
3
0
.
8
4
8
7
5
W
L
A
N
1
4
0
.
0
8
4
8
7
5
.
3
.
F
o
r
m
o
bil
e
no
de
m
o
v
e
m
e
nt
f
ro
m
C
t
o
D
I
n
F
ig
u
r
e
6
is
s
i
m
ilar
l
y
,
w
h
e
n
a
m
o
b
ile
n
o
d
e
m
o
v
es
f
r
o
m
p
o
in
t
C
to
p
o
in
t
D
.
Han
d
o
f
f
Facto
r
o
f
Wi
-
M
A
X
3
is
h
i
g
h
as
co
m
p
ar
ed
to
o
th
er
elig
ib
le
ca
n
d
id
at
e
n
et
w
o
r
k
s
.
T
h
er
ef
o
r
e,
W
i
-
M
AX
3
is
a
p
r
ef
er
ab
le
n
et
w
o
r
k
at
p
o
in
t D
b
ec
au
s
e
o
f
its
h
i
g
h
Han
d
o
f
f
Fac
to
r
as sh
o
w
n
i
n
T
ab
le
5
.
F
ig
u
re
6
.
M
o
b
il
e
n
o
d
e
m
o
v
e
m
e
n
t
f
ro
m
p
o
in
t
C
t
o
D
T
ab
le
5
.
P
r
ef
er
r
ed
n
et
w
o
r
k
(
M
o
b
ile
n
o
d
e
m
o
v
e
m
e
n
t
f
r
o
m
p
o
in
t
C
to
D)
M
o
b
i
l
e
N
o
d
e
L
o
c
a
t
i
o
n
WB
WC
WB
C
a
n
d
i
d
a
t
e
N
e
t
w
o
r
k
H
a
n
d
o
f
f
f
a
c
t
o
r
P
r
e
f
e
r
r
e
d
N
e
t
w
o
r
k
D
i
st
a
n
c
e
w
h
i
c
h
H
a
n
d
o
f
f
O
c
c
u
r
D
0
.
0
2
5
0
0
.
5
0
4
7
0
.
3
8
9
3
Wi
-
M
A
X
2
1
.
4
9
1
1
Wi
-
M
A
X
3
4
0
0
.
3
0
0
Wi
-
M
A
X
3
1
.
5
3
8
4
W
L
A
N
3
1
.
1
2
3
W
L
A
N
4
1
.
3
1
8
1
W
L
A
N
8
1
.
1
9
5
1
W
L
A
N
1
2
1
.
0
9
8
3
W
L
A
N
1
3
1
.
1
3
4
5
W
L
A
N
1
4
1
.
4
8
4
6.
CO
NCLU
SI
O
NS
I
n
th
i
s
ch
ap
ter
,
a
m
et
h
o
d
to
s
elec
t
an
o
p
ti
m
u
m
tar
g
et
n
e
t
w
o
r
k
is
p
r
esen
ted
.
T
h
is
m
e
th
o
d
is
b
ased
o
n
W
eig
h
t
s
E
s
ti
m
atio
n
an
d
Ha
n
d
o
f
f
Facto
r
E
s
ti
m
atio
n
alg
o
r
i
th
m
.
W
ei
g
h
ts
o
f
i
n
p
u
t
d
ec
is
i
o
n
p
ar
am
e
ter
s
ar
e
ca
lcu
lated
,
an
d
h
a
n
d
o
f
f
f
ac
to
r
s
o
f
ca
n
d
id
ate
n
et
w
o
r
k
s
a
r
e
ca
lcu
lated
u
s
i
n
g
a
co
s
t
f
u
n
ct
io
n
.
T
h
e
n
et
w
o
r
k
w
i
th
th
e
h
ig
h
e
s
t
h
an
d
o
f
f
f
ac
to
r
is
s
elec
ted
as
th
e
h
a
n
d
o
v
er
tar
g
e
t
.
T
h
is
m
et
h
o
d
is
ab
le
to
p
r
o
v
i
d
e
th
e
al
w
a
y
s
b
es
t
co
n
n
ec
ted
s
er
v
ice
s
to
u
s
er
s
.
Hen
ce
en
h
an
ce
s
u
s
er
’
s
s
at
is
f
ac
t
io
n
le
v
el
co
m
p
ar
in
g
with
m
et
h
o
d
s
t
h
at
co
n
s
is
ten
tl
y
c
h
o
o
s
e
o
n
e
ac
ce
s
s
n
et
w
o
r
k
.
RE
F
E
R
E
NC
E
S
[1
]
G
a
z
is,
V
.
,
A
lo
n
isti
o
ti
,
N.
a
n
d
M
e
ra
k
o
s,
L
.
,
“
T
o
wa
rd
a
g
e
n
e
ric
a
lwa
y
s
b
e
st
c
o
n
n
e
c
ted
c
a
p
a
b
il
it
y
in
in
teg
ra
ted
WL
A
N/UMT
S
c
e
ll
u
l
a
r
m
o
b
il
e
n
e
tw
o
rk
s
(a
n
d
b
e
y
o
n
d
),
”
IEE
E
W
ire
les
s
Co
mm
u
n
ica
ti
o
n
s
,
v
o
l.
1
2
,
n
o
.
3
,
p
p
.
2
0
-
2
9
,
2
0
0
5
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t
J
R
o
b
&
A
u
to
m
I
SS
N:
2089
-
4856
A
n
ew me
th
o
d
fo
r
h
a
n
d
o
ff ta
r
g
et
n
etw
o
r
k
s
elec
tio
n
(
Jy
o
ti M
a
d
a
a
n
)
43
[2
]
Du
tt
a
,
A
.
,
Da
s,
S
.
,
F
a
m
o
lari,
D.,
Oh
b
a
,
Y.,
T
a
n
iu
c
h
i,
K.,
F
a
jard
o
,
V
.
,
a
n
d
S
c
h
u
lzri
n
n
e
,
H.,
“
S
e
a
m
le
ss
p
ro
a
c
ti
v
e
h
a
n
d
o
v
e
r
a
c
ro
s
s
h
e
tero
g
e
n
e
o
u
s
a
c
c
e
ss
n
e
t
w
o
rk
s,”
W
ire
l
e
ss
Per
so
n
a
l
Co
mm
u
n
ica
ti
o
n
s
,
v
o
l.
4
3
,
n
o
.
3
,
p
p
.
8
3
7
-
8
5
5
,
2
0
0
7
.
[3
]
W
a
n
g
,
H.J.,
Ka
tz,
R.
H.
a
n
d
G
ies
e
,
J.,
F
e
b
ru
a
ry
.
“
Po
li
c
y
-
e
n
a
b
led
h
a
n
d
o
ff
s
a
c
ro
ss
h
e
ter
o
g
e
n
e
o
u
s
wire
les
s
n
e
two
rk
s
,
”
In
S
e
c
o
n
d
IEE
E
W
o
r
k
sh
o
p
o
n
M
o
b
il
e
Co
m
p
u
ti
n
g
S
y
s
tem
s
a
n
d
A
p
p
li
c
a
ti
o
n
s
(W
M
CS
A
'
9
9
),
p
p
.
51
-
6
0
,
IEE
E
P
r
o
c
e
e
d
in
g
s
,
1
9
9
9
.
[4
]
Zh
u
,
F
.
a
n
d
M
c
Na
ir,
J.,
M
a
rc
h
.
“
Op
ti
miza
ti
o
n
s
f
o
r
v
e
rtica
l
h
a
n
d
o
ff
d
e
c
isio
n
a
l
g
o
rit
h
m
s
,
”
In
W
irele
ss
c
o
m
m
u
n
ica
ti
o
n
s an
d
n
e
tw
o
rk
in
g
c
o
n
f
e
re
n
c
e
(W
C
NC),
v
o
l.
2
,
p
p
.
8
6
7
-
8
7
2
,
IEE
E
,
2
0
0
4
.
[5
]
Zh
u
,
F
.
a
n
d
M
c
Na
ir,
J.,
“
M
u
l
ti
se
rv
ice
v
e
rti
c
a
l
h
a
n
d
o
f
f
d
e
c
isio
n
a
lg
o
rit
h
m
s,
”
EURA
S
IP
J
o
u
rn
a
l
o
n
wire
les
s
c
o
mm
u
n
ica
t
io
n
s a
n
d
n
e
two
rk
in
g
,
v
o
l.
2
,
p
p
.
5
2
-
52
,
2
0
0
6
.
[6
]
L
e
e
,
W
.
,
Ki
m
,
E.
,
Kim
,
J.,
L
e
e
,
I
.
a
n
d
L
e
e
,
C.
,
“
M
o
v
e
m
e
n
t
-
a
w
a
re
v
e
rti
c
a
l
h
a
n
d
o
f
f
o
f
W
LAN
a
n
d
m
o
b
il
e
W
iM
A
X
f
o
r
se
a
m
le
ss
u
b
iq
u
it
o
u
s ac
c
e
ss
,
”
IEE
E
T
ra
n
sa
c
ti
o
n
s
o
n
Co
n
s
u
me
r E
lec
tro
n
ics
,
v
o
l.
5
3
,
n
o
.
4
,
2
0
0
7
.
[7
]
He
,
D.,
Ch
i
,
C.
,
Ch
a
n
,
S
.
,
C
h
e
n
,
C.
,
B
u
,
J.
a
n
d
Yi
n
,
M
.
,
“
A
sim
p
le
a
n
d
ro
b
u
st
v
e
rti
c
a
l
h
a
n
d
o
f
f
a
lg
o
rit
h
m
f
o
r
h
e
tero
g
e
n
e
o
u
s w
irele
ss
m
o
b
il
e
n
e
tw
o
rk
s,”
W
ir
e
les
s P
e
r
so
n
a
l
C
o
m
mu
n
ica
t
io
n
s
,
v
o
l.
5
9
,
n
o
.
2
,
p
p
.
3
6
1
-
3
7
3
,
2
0
1
1
.
[8
]
Ku
n
a
ra
k
,
S
.
a
n
d
S
u
lee
sa
th
ira,
R.
“
A
l
g
o
rit
h
m
ic
v
e
rti
c
a
l
h
a
n
d
o
ff
d
e
c
isio
n
a
n
d
m
e
rit
n
e
tw
o
rk
s
e
lec
ti
o
n
a
c
ro
ss
h
e
tero
g
e
n
e
o
u
s w
irele
ss
n
e
tw
o
rk
s,
”
W
S
EA
S
T
r
a
n
sa
c
t
io
n
s o
n
Co
mm
u
n
ica
ti
o
n
s,
v
o
l.
1
2
,
n
o
.
1
,
p
p
.
1
-
1
3
,
2
0
1
3
.
[9
]
X
ia,
L
.
,
L
in
g
-
g
e
,
J.,
C
h
e
n
,
H.
a
n
d
H
o
n
g
-
W
e
i,
L
.
,
“
An
in
telli
g
e
n
t
v
e
rtica
l
h
a
n
d
o
ff
a
l
g
o
rit
h
m
i
n
h
e
ter
o
g
e
n
e
o
u
s
wire
les
s
n
e
two
rk
s
,
”
In
In
tern
a
ti
o
n
a
l
Co
n
f
e
re
n
c
e
o
n
Ne
u
ra
l
Ne
tw
o
rk
s
a
n
d
S
ig
n
a
l
P
r
o
c
e
ss
in
g
,
p
p
.
5
5
0
-
555
,
IEE
E
,
Ju
n
e
,
2
0
0
8
.
[1
0
]
Na
ss
e
r,
N.,
Ha
ss
w
a
,
A
.
,
&
Ha
ss
a
n
e
in
,
H.,
“
Ha
n
d
o
f
f
s
in
f
o
u
rth
g
e
n
e
ra
ti
o
n
h
e
tero
g
e
n
e
o
u
s
n
e
t
w
o
rk
s,
”
IEE
E
Co
mm
u
n
ica
ti
o
n
s M
a
g
a
zin
e
,
v
o
l.
4
4
,
n
o
.
1
0
,
p
p
.
9
5
–
1
0
3
,
2
0
0
6
.
[1
1
]
Qu
a
li
ty
o
f
se
r
v
ice
.
UM
T
S
.
[
O
n
li
n
e
]
.
A
v
a
il
a
b
le:
h
tt
p
:/
/w
ww
.
u
m
ts
wo
rld
.
c
o
m
/t
e
c
h
n
o
lo
g
y
/q
o
s.h
tm
.
Evaluation Warning : The document was created with Spire.PDF for Python.